Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows72
Duplicate rows (%)0.6%
Total size in memory1.7 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

Dataset has 72 (0.6%) duplicate rowsDuplicates
Administrative is highly overall correlated with Administrative_DurationHigh correlation
Administrative_Duration is highly overall correlated with AdministrativeHigh correlation
Informational is highly overall correlated with Informational_DurationHigh correlation
Informational_Duration is highly overall correlated with InformationalHigh correlation
ProductRelated is highly overall correlated with ProductRelated_Duration and 1 other fieldsHigh correlation
ProductRelated_Duration is highly overall correlated with ProductRelatedHigh correlation
BounceRates is highly overall correlated with ExitRatesHigh correlation
ExitRates is highly overall correlated with ProductRelated and 1 other fieldsHigh correlation
VisitorType is highly imbalanced (59.9%)Imbalance
Administrative has 5768 (46.8%) zerosZeros
Administrative_Duration has 5903 (47.9%) zerosZeros
Informational has 9699 (78.7%) zerosZeros
Informational_Duration has 9967 (80.8%) zerosZeros
ProductRelated_Duration has 722 (5.9%) zerosZeros
BounceRates has 5518 (44.8%) zerosZeros
PageValues has 9600 (77.9%) zerosZeros
SpecialDay has 11079 (89.9%) zerosZeros

Reproduction

Analysis started2023-07-17 08:49:25.976483
Analysis finished2023-07-17 08:50:37.999628
Duration1 minute and 12.02 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Administrative
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3151663
Minimum0
Maximum27
Zeros5768
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:38.138173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3217841
Coefficient of variation (CV)1.4347929
Kurtosis4.7011462
Mean2.3151663
Median Absolute Deviation (MAD)1
Skewness1.9603572
Sum28546
Variance11.03425
MonotonicityNot monotonic
2023-07-17T08:50:38.425215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 5768
46.8%
1 1354
 
11.0%
2 1114
 
9.0%
3 915
 
7.4%
4 765
 
6.2%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.7%
8 287
 
2.3%
9 225
 
1.8%
Other values (17) 557
 
4.5%
ValueCountFrequency (%)
0 5768
46.8%
1 1354
 
11.0%
2 1114
 
9.0%
3 915
 
7.4%
4 765
 
6.2%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.7%
8 287
 
2.3%
9 225
 
1.8%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 4
 
< 0.1%
23 3
 
< 0.1%
22 4
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 6
 
< 0.1%
18 12
0.1%
17 16
0.1%

Administrative_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3335
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.818611
Minimum0
Maximum3398.75
Zeros5903
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:38.721306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q393.25625
95-th percentile348.26637
Maximum3398.75
Range3398.75
Interquartile range (IQR)93.25625

Descriptive statistics

Standard deviation176.77911
Coefficient of variation (CV)2.1873564
Kurtosis50.556739
Mean80.818611
Median Absolute Deviation (MAD)7.5
Skewness5.615719
Sum996493.47
Variance31250.853
MonotonicityNot monotonic
2023-07-17T08:50:39.011314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5903
47.9%
4 56
 
0.5%
5 53
 
0.4%
7 45
 
0.4%
11 42
 
0.3%
6 41
 
0.3%
14 37
 
0.3%
9 35
 
0.3%
15 33
 
0.3%
10 32
 
0.3%
Other values (3325) 6053
49.1%
ValueCountFrequency (%)
0 5903
47.9%
1.333333333 1
 
< 0.1%
2 15
 
0.1%
3 26
 
0.2%
3.5 4
 
< 0.1%
4 56
 
0.5%
4.333333333 1
 
< 0.1%
4.5 2
 
< 0.1%
4.75 1
 
< 0.1%
5 53
 
0.4%
ValueCountFrequency (%)
3398.75 1
< 0.1%
2720.5 1
< 0.1%
2657.318056 1
< 0.1%
2629.253968 1
< 0.1%
2407.42381 1
< 0.1%
2156.166667 1
< 0.1%
2137.112745 1
< 0.1%
2086.75 1
< 0.1%
2047.234848 1
< 0.1%
1951.279141 1
< 0.1%

Informational
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50356853
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:39.279419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2701564
Coefficient of variation (CV)2.522311
Kurtosis26.932266
Mean0.50356853
Median Absolute Deviation (MAD)0
Skewness4.0364638
Sum6209
Variance1.6132973
MonotonicityNot monotonic
2023-07-17T08:50:39.530932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
9 15
 
0.1%
8 14
 
0.1%
Other values (7) 18
 
0.1%
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
8 14
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 7
 
0.1%
9 15
0.1%
8 14
 
0.1%
7 36
0.3%

Informational_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1241
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.164066
Minimum0
Maximum2549.375
Zeros9967
Zeros (%)80.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:40.022520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile193.775
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.57594
Coefficient of variation (CV)4.114731
Kurtosis76.747994
Mean34.164066
Median Absolute Deviation (MAD)0
Skewness7.606278
Sum421242.93
Variance19761.594
MonotonicityNot monotonic
2023-07-17T08:50:40.474534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9967
80.8%
9 32
 
0.3%
7 25
 
0.2%
6 25
 
0.2%
10 25
 
0.2%
12 23
 
0.2%
16 22
 
0.2%
13 22
 
0.2%
8 21
 
0.2%
11 20
 
0.2%
Other values (1231) 2148
 
17.4%
ValueCountFrequency (%)
0 9967
80.8%
1 3
 
< 0.1%
1.5 1
 
< 0.1%
2 11
 
0.1%
2.5 1
 
< 0.1%
3 16
 
0.1%
3.5 1
 
< 0.1%
4 17
 
0.1%
5 18
 
0.1%
5.5 3
 
< 0.1%
ValueCountFrequency (%)
2549.375 1
< 0.1%
2256.916667 1
< 0.1%
2252.033333 1
< 0.1%
2195.3 1
< 0.1%
2166.5 1
< 0.1%
2050.433333 1
< 0.1%
1949.166667 1
< 0.1%
1830.5 1
< 0.1%
1779.166667 1
< 0.1%
1778 1
< 0.1%

ProductRelated
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.731468
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:40.994254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.475503
Coefficient of variation (CV)1.4016214
Kurtosis31.211707
Mean31.731468
Median Absolute Deviation (MAD)13
Skewness4.3415164
Sum391249
Variance1978.0704
MonotonicityNot monotonic
2023-07-17T08:50:41.517030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 622
 
5.0%
2 465
 
3.8%
3 458
 
3.7%
4 404
 
3.3%
6 396
 
3.2%
7 391
 
3.2%
5 382
 
3.1%
8 370
 
3.0%
10 330
 
2.7%
9 317
 
2.6%
Other values (301) 8195
66.5%
ValueCountFrequency (%)
0 38
 
0.3%
1 622
5.0%
2 465
3.8%
3 458
3.7%
4 404
3.3%
5 382
3.1%
6 396
3.2%
7 391
3.2%
8 370
3.0%
9 317
2.6%
ValueCountFrequency (%)
705 1
< 0.1%
686 1
< 0.1%
584 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
501 1
< 0.1%
486 1
< 0.1%
470 1
< 0.1%
449 1
< 0.1%

ProductRelated_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9210
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1166.9243
Minimum0
Maximum63973.522
Zeros722
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:42.072802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1196.5
median597.625
Q31406.1821
95-th percentile4206.2117
Maximum63973.522
Range63973.522
Interquartile range (IQR)1209.6821

Descriptive statistics

Standard deviation1873.6506
Coefficient of variation (CV)1.6056316
Kurtosis147.95322
Mean1166.9243
Median Absolute Deviation (MAD)482.44048
Skewness7.5395098
Sum14388177
Variance3510566.4
MonotonicityNot monotonic
2023-07-17T08:50:42.566293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 722
 
5.9%
597.625 492
 
4.0%
17 19
 
0.2%
11 17
 
0.1%
15 16
 
0.1%
8 16
 
0.1%
12 15
 
0.1%
19 15
 
0.1%
22 14
 
0.1%
7 14
 
0.1%
Other values (9200) 10990
89.1%
ValueCountFrequency (%)
0 722
5.9%
0.5 1
 
< 0.1%
1 2
 
< 0.1%
2.333333333 1
 
< 0.1%
2.666666667 1
 
< 0.1%
3 4
 
< 0.1%
4 9
 
0.1%
5 12
 
0.1%
5.333333333 1
 
< 0.1%
6 5
 
< 0.1%
ValueCountFrequency (%)
63973.52223 1
< 0.1%
43171.23338 1
< 0.1%
29970.46597 1
< 0.1%
27009.85943 1
< 0.1%
24844.1562 1
< 0.1%
23888.81 1
< 0.1%
23342.08205 1
< 0.1%
23050.10414 1
< 0.1%
21672.24425 1
< 0.1%
18504.12621 1
< 0.1%

BounceRates
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1872
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219138
Minimum0
Maximum0.2
Zeros5518
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:42.960080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0031124675
Q30.016812558
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.016812558

Descriptive statistics

Standard deviation0.048488322
Coefficient of variation (CV)2.185007
Kurtosis7.7231594
Mean0.02219138
Median Absolute Deviation (MAD)0.0031124675
Skewness2.9478553
Sum273.61972
Variance0.0023511174
MonotonicityNot monotonic
2023-07-17T08:50:43.280949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5518
44.8%
0.2 700
 
5.7%
0.066666667 134
 
1.1%
0.028571429 115
 
0.9%
0.05 113
 
0.9%
0.033333333 101
 
0.8%
0.025 100
 
0.8%
0.016666667 99
 
0.8%
0.1 98
 
0.8%
0.04 96
 
0.8%
Other values (1862) 5256
42.6%
ValueCountFrequency (%)
0 5518
44.8%
2.73 × 10-51
 
< 0.1%
3.35 × 10-51
 
< 0.1%
3.83 × 10-51
 
< 0.1%
3.94 × 10-51
 
< 0.1%
7.09 × 10-51
 
< 0.1%
7.27 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.2 700
5.7%
0.183333333 1
 
< 0.1%
0.18 5
 
< 0.1%
0.176923077 1
 
< 0.1%
0.175 1
 
< 0.1%
0.166666667 4
 
< 0.1%
0.164285714 1
 
< 0.1%
0.164230769 1
 
< 0.1%
0.161904762 1
 
< 0.1%
0.16 3
 
< 0.1%

ExitRates
Real number (ℝ)

HIGH CORRELATION 

Distinct4746
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.042877478
Minimum0
Maximum0.2
Zeros74
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:43.612048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0046371831
Q10.014285714
median0.025141026
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.048393122
Coefficient of variation (CV)1.1286373
Kurtosis4.1082688
Mean0.042877478
Median Absolute Deviation (MAD)0.014029915
Skewness2.1675868
Sum528.67931
Variance0.0023418943
MonotonicityNot monotonic
2023-07-17T08:50:43.980767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 704
 
5.7%
0.1 331
 
2.7%
0.05 327
 
2.7%
0.033333333 289
 
2.3%
0.066666667 267
 
2.2%
0.025 222
 
1.8%
0.04 214
 
1.7%
0.016666667 181
 
1.5%
0.02 166
 
1.3%
0.022222222 151
 
1.2%
Other values (4736) 9478
76.9%
ValueCountFrequency (%)
0 74
0.6%
0.000175593 1
 
< 0.1%
0.000250438 1
 
< 0.1%
0.000262123 1
 
< 0.1%
0.000263158 1
 
< 0.1%
0.000292398 1
 
< 0.1%
0.000409836 1
 
< 0.1%
0.000446429 1
 
< 0.1%
0.000468384 1
 
< 0.1%
0.000480769 1
 
< 0.1%
ValueCountFrequency (%)
0.2 704
5.7%
0.192307692 1
 
< 0.1%
0.188888889 2
 
< 0.1%
0.186666667 4
 
< 0.1%
0.183333333 2
 
< 0.1%
0.181818182 1
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 3
 
< 0.1%
0.177777778 5
 
< 0.1%
0.175 6
 
< 0.1%

PageValues
Real number (ℝ)

ZEROS 

Distinct2704
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8892579
Minimum0
Maximum361.76374
Zeros9600
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:44.331191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.160528
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.568437
Coefficient of variation (CV)3.1529332
Kurtosis65.635694
Mean5.8892579
Median Absolute Deviation (MAD)0
Skewness6.3829642
Sum72614.549
Variance344.78684
MonotonicityNot monotonic
2023-07-17T08:50:44.642610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9600
77.9%
53.988 6
 
< 0.1%
42.29306752 3
 
< 0.1%
59.988 2
 
< 0.1%
16.1585582 2
 
< 0.1%
44.89345937 2
 
< 0.1%
14.1273698 2
 
< 0.1%
34.03997536 2
 
< 0.1%
10.99901844 2
 
< 0.1%
58.9241766 2
 
< 0.1%
Other values (2694) 2707
 
22.0%
ValueCountFrequency (%)
0 9600
77.9%
0.038034542 1
 
< 0.1%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.098621403 1
 
< 0.1%
0.120699914 1
 
< 0.1%
0.129676893 1
 
< 0.1%
0.131837013 1
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
< 0.1%
360.9533839 1
< 0.1%
287.9537928 1
< 0.1%
270.7846931 1
< 0.1%
261.4912857 1
< 0.1%
258.5498732 1
< 0.1%
255.5691579 1
< 0.1%
254.6071579 1
< 0.1%
246.7585902 1
< 0.1%
239.98 1
< 0.1%

SpecialDay
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061427413
Minimum0
Maximum1
Zeros11079
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:44.932247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19891727
Coefficient of variation (CV)3.2382492
Kurtosis9.9136589
Mean0.061427413
Median Absolute Deviation (MAD)0
Skewness3.3026667
Sum757.4
Variance0.039568082
MonotonicityNot monotonic
2023-07-17T08:50:45.176301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11079
89.9%
0.6 351
 
2.8%
0.8 325
 
2.6%
0.4 243
 
2.0%
0.2 178
 
1.4%
1 154
 
1.2%
ValueCountFrequency (%)
0 11079
89.9%
0.2 178
 
1.4%
0.4 243
 
2.0%
0.6 351
 
2.8%
0.8 325
 
2.6%
1 154
 
1.2%
ValueCountFrequency (%)
1 154
 
1.2%
0.8 325
 
2.6%
0.6 351
 
2.8%
0.4 243
 
2.0%
0.2 178
 
1.4%
0 11079
89.9%

Month
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6509327
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:45.421045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median8
Q311
95-th percentile12
Maximum12
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3827128
Coefficient of variation (CV)0.44213078
Kurtosis-1.6077617
Mean7.6509327
Median Absolute Deviation (MAD)3
Skewness-0.055795757
Sum94336
Variance11.442746
MonotonicityNot monotonic
2023-07-17T08:50:45.675758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 3348
27.2%
11 2980
24.2%
3 1897
15.4%
12 1713
13.9%
10 545
 
4.4%
8 501
 
4.1%
9 446
 
3.6%
7 429
 
3.5%
6 288
 
2.3%
2 183
 
1.5%
ValueCountFrequency (%)
2 183
 
1.5%
3 1897
15.4%
5 3348
27.2%
6 288
 
2.3%
7 429
 
3.5%
8 501
 
4.1%
9 446
 
3.6%
10 545
 
4.4%
11 2980
24.2%
12 1713
13.9%
ValueCountFrequency (%)
12 1713
13.9%
11 2980
24.2%
10 545
 
4.4%
9 446
 
3.6%
8 501
 
4.1%
7 429
 
3.5%
6 288
 
2.3%
5 3348
27.2%
3 1897
15.4%
2 183
 
1.5%

OperatingSystems
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1240065
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:45.949243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91132483
Coefficient of variation (CV)0.42905934
Kurtosis10.456843
Mean2.1240065
Median Absolute Deviation (MAD)0
Skewness2.066285
Sum26189
Variance0.83051294
MonotonicityNot monotonic
2023-07-17T08:50:46.195251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6601
53.5%
1 2585
 
21.0%
3 2555
 
20.7%
4 478
 
3.9%
8 79
 
0.6%
6 19
 
0.2%
7 7
 
0.1%
5 6
 
< 0.1%
ValueCountFrequency (%)
1 2585
 
21.0%
2 6601
53.5%
3 2555
 
20.7%
4 478
 
3.9%
5 6
 
< 0.1%
6 19
 
0.2%
7 7
 
0.1%
8 79
 
0.6%
ValueCountFrequency (%)
8 79
 
0.6%
7 7
 
0.1%
6 19
 
0.2%
5 6
 
< 0.1%
4 478
 
3.9%
3 2555
 
20.7%
2 6601
53.5%
1 2585
 
21.0%

Browser
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3570965
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:46.468827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7172767
Coefficient of variation (CV)0.72855594
Kurtosis12.746733
Mean2.3570965
Median Absolute Deviation (MAD)0
Skewness3.2423496
Sum29063
Variance2.9490392
MonotonicityNot monotonic
2023-07-17T08:50:46.717889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 7961
64.6%
1 2462
 
20.0%
4 736
 
6.0%
5 467
 
3.8%
6 174
 
1.4%
10 163
 
1.3%
8 135
 
1.1%
3 105
 
0.9%
13 61
 
0.5%
7 49
 
0.4%
Other values (3) 17
 
0.1%
ValueCountFrequency (%)
1 2462
 
20.0%
2 7961
64.6%
3 105
 
0.9%
4 736
 
6.0%
5 467
 
3.8%
6 174
 
1.4%
7 49
 
0.4%
8 135
 
1.1%
9 1
 
< 0.1%
10 163
 
1.3%
ValueCountFrequency (%)
13 61
 
0.5%
12 10
 
0.1%
11 6
 
< 0.1%
10 163
 
1.3%
9 1
 
< 0.1%
8 135
 
1.1%
7 49
 
0.4%
6 174
 
1.4%
5 467
3.8%
4 736
6.0%

Region
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1473642
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:46.958810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4015912
Coefficient of variation (CV)0.76304842
Kurtosis-0.1486803
Mean3.1473642
Median Absolute Deviation (MAD)2
Skewness0.98354916
Sum38807
Variance5.7676405
MonotonicityNot monotonic
2023-07-17T08:50:47.182383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4780
38.8%
3 2403
19.5%
4 1182
 
9.6%
2 1136
 
9.2%
6 805
 
6.5%
7 761
 
6.2%
9 511
 
4.1%
8 434
 
3.5%
5 318
 
2.6%
ValueCountFrequency (%)
1 4780
38.8%
2 1136
 
9.2%
3 2403
19.5%
4 1182
 
9.6%
5 318
 
2.6%
6 805
 
6.5%
7 761
 
6.2%
8 434
 
3.5%
9 511
 
4.1%
ValueCountFrequency (%)
9 511
 
4.1%
8 434
 
3.5%
7 761
 
6.2%
6 805
 
6.5%
5 318
 
2.6%
4 1182
 
9.6%
3 2403
19.5%
2 1136
 
9.2%
1 4780
38.8%

TrafficType
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0695864
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-07-17T08:50:47.440682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0251692
Coefficient of variation (CV)0.98908557
Kurtosis3.4797106
Mean4.0695864
Median Absolute Deviation (MAD)1
Skewness1.9629867
Sum50178
Variance16.201987
MonotonicityNot monotonic
2023-07-17T08:50:47.674795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 3913
31.7%
1 2451
19.9%
3 2052
16.6%
4 1069
 
8.7%
13 738
 
6.0%
10 450
 
3.6%
6 444
 
3.6%
8 343
 
2.8%
5 260
 
2.1%
11 247
 
2.0%
Other values (10) 363
 
2.9%
ValueCountFrequency (%)
1 2451
19.9%
2 3913
31.7%
3 2052
16.6%
4 1069
 
8.7%
5 260
 
2.1%
6 444
 
3.6%
7 40
 
0.3%
8 343
 
2.8%
9 42
 
0.3%
10 450
 
3.6%
ValueCountFrequency (%)
20 198
 
1.6%
19 17
 
0.1%
18 10
 
0.1%
17 1
 
< 0.1%
16 3
 
< 0.1%
15 38
 
0.3%
14 13
 
0.1%
13 738
6.0%
12 1
 
< 0.1%
11 247
 
2.0%

VisitorType
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
1
10551 
2
1694 
3
 
85

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10551
85.6%
2 1694
 
13.7%
3 85
 
0.7%

Length

2023-07-17T08:50:47.957799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T08:50:48.216833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10551
85.6%
2 1694
 
13.7%
3 85
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 10551
85.6%
2 1694
 
13.7%
3 85
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10551
85.6%
2 1694
 
13.7%
3 85
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10551
85.6%
2 1694
 
13.7%
3 85
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10551
85.6%
2 1694
 
13.7%
3 85
 
0.7%

Weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
9462 
1
2868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Length

2023-07-17T08:50:48.437946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T08:50:48.691896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Revenue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
10422 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Length

2023-07-17T08:50:48.904265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T08:50:49.177274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Interactions

2023-07-17T08:50:32.163950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:27.301462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:31.198941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:37.494846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:42.562584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:46.440689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:51.872111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:55.841519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:00.286241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:05.620682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:09.521887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:13.723437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:19.012212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:23.011906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:27.351840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:32.417936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:27.549685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:31.548015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:37.741410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:42.808883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:46.823053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:52.149783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:56.097459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:00.702236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:05.880426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:09.777654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:14.115692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:19.264389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:23.296699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:27.761064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:32.684078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:27.809144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:31.901269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:39.170081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:43.061449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:47.244068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:52.416352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:56.368585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:01.128345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:06.140352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:10.034823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:14.525980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:19.536840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:23.576121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:28.186500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:32.941340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:28.064309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:32.336923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:39.447749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:43.320838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:47.649372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:52.661701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:56.629254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:01.536323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:06.388648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:10.282940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:14.912905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:19.794338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:23.839451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:28.603978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:33.196932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:28.313941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:32.846015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:39.720070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:43.550448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:48.063020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:52.905929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:56.884586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:02.437623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:06.638531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:10.527854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:15.257934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:20.055095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:24.104545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:29.020697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:33.474634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:28.568656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:33.610619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:39.990627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:43.810326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:48.486443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:53.177510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:57.154058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:02.836929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:06.891612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:10.798617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:15.679287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:20.328011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:24.367643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:29.480310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:33.747580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:28.818682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:34.446979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:40.250236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:44.060270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:48.904156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:53.437390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:57.428427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:03.106000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:07.149589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:11.063803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:16.117942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:20.596758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:24.619533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:29.746599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:34.034443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:29.093851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:34.879817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:40.511928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:44.323483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:49.702038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:53.707583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:57.718372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:03.392313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:07.417079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:11.336374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:16.441632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:20.869423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:24.885261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:30.018550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:34.307427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:29.361521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:35.322176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:40.776295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:44.583410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:49.989417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:53.985352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:58.034384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:03.684372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:07.695224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:11.604818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:16.748602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:21.153740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:25.165368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:30.298736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:34.587451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:29.610904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:35.743378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:41.029192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:44.839083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:50.254559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:54.276229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:58.334701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:03.945456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:07.958405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:11.866032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:17.022096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:21.436738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:25.422436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:30.577579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:34.839849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:29.846941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:36.144692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:41.275952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:45.078052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:50.505465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:54.530810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:58.604183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:04.203594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:08.207922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:12.114588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:17.269618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:21.689507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:25.675565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:30.833948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:35.097346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:30.104130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:36.411927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:41.523522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:45.320981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:50.757135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:54.778232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:58.876862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:04.470083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:08.461997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:12.367786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:17.960441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:21.942894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:25.916081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:31.096071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:35.368177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:30.356484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:36.672780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:41.791651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:45.570350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:51.025254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:55.046129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:59.170594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:04.756647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:08.733624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:12.623793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:18.223548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:22.226670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:26.185385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:31.367071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:35.642514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:30.603081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:36.933026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:42.030276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:45.820197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:51.303891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:55.317665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:59.453585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:05.037820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:08.989643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:12.908157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:18.469229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:22.480927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:26.544583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:31.625913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:35.909320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:30.866777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:37.219645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:42.303251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:46.081158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:51.598057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:55.581207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:49:59.839647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:05.329834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:09.258461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:13.307213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:18.736256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:22.741303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:26.927280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T08:50:31.894666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-17T08:50:49.379651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
Administrative1.0000.9410.3690.3600.4600.410-0.155-0.4320.328-0.1250.081-0.005-0.0120.009-0.0120.0860.0280.131
Administrative_Duration0.9411.0000.3570.3490.4300.402-0.164-0.4360.317-0.1320.081-0.007-0.0230.019-0.0150.0070.0000.064
Informational0.3690.3571.0000.9410.3690.3570.006-0.1850.219-0.0540.0590.000-0.020-0.023-0.0290.0280.0110.078
Informational_Duration0.3600.3490.9411.0000.3580.348-0.003-0.1980.222-0.0550.0510.003-0.013-0.015-0.0240.0070.0000.067
ProductRelated0.4600.4300.3690.3581.0000.862-0.052-0.5160.342-0.0220.1400.0210.044-0.021-0.0700.0790.0000.127
ProductRelated_Duration0.4100.4020.3570.3480.8621.000-0.073-0.4600.352-0.0490.1300.0230.046-0.010-0.0740.0340.0040.071
BounceRates-0.155-0.1640.006-0.003-0.052-0.0731.0000.599-0.1240.135-0.0050.053-0.047-0.0180.0160.1230.0500.170
ExitRates-0.432-0.436-0.185-0.198-0.516-0.4600.5991.000-0.3060.150-0.0640.023-0.015-0.0050.0210.1820.0650.243
PageValues0.3280.3170.2190.2220.3420.352-0.124-0.3061.000-0.0700.061-0.0120.0260.001-0.0180.1100.0310.413
SpecialDay-0.125-0.132-0.054-0.055-0.022-0.0490.1350.150-0.0701.000-0.2520.0230.021-0.0150.1100.0640.2590.086
Month0.0810.0810.0590.0510.1400.130-0.005-0.0640.061-0.2521.0000.005-0.0220.018-0.0180.1350.0530.172
OperatingSystems-0.005-0.0070.0000.0030.0210.0230.0530.023-0.0120.0230.0051.0000.3750.0270.0800.4650.1180.074
Browser-0.012-0.023-0.020-0.0130.0440.046-0.047-0.0150.0260.021-0.0220.3751.0000.0550.0000.4720.0590.038
Region0.0090.019-0.023-0.015-0.021-0.010-0.018-0.0050.001-0.0150.0180.0270.0551.000-0.0040.1800.0170.010
TrafficType-0.012-0.015-0.029-0.024-0.070-0.0740.0160.021-0.0180.110-0.0180.0800.000-0.0041.0000.3160.0920.121
VisitorType0.0860.0070.0280.0070.0790.0340.1230.1820.1100.0640.1350.4650.4720.1800.3161.0000.0540.104
Weekend0.0280.0000.0110.0000.0000.0040.0500.0650.0310.2590.0530.1180.0590.0170.0920.0541.0000.028
Revenue0.1310.0640.0780.0670.1270.0710.1700.2430.4130.0860.1720.0740.0380.0100.1210.1040.0281.000

Missing values

2023-07-17T08:50:37.063059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T08:50:37.698190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.021111100
100.000.0264.0000000.0000000.1000000.00.022212100
200.000.010.0000000.2000000.2000000.00.024193100
300.000.022.6666670.0500000.1400000.00.023224100
400.000.010627.5000000.0200000.0500000.00.023314110
500.000.019154.2166670.0157890.0245610.00.022213100
600.000.010.0000000.2000000.2000000.00.422433100
710.000.000.0000000.2000000.2000000.00.021215110
800.000.0237.0000000.0000000.0251410.00.822223100
900.000.03738.0000000.0000000.0222220.00.422412100
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.0112231100
1232100.0000.060.0000000.2000000.2000000.0000000.0111841100
12322676.2500.0221075.2500000.0000000.0041670.0000000.0122242100
12323264.7500.0441157.9761900.0000000.0139530.0000000.01122110100
1232400.0010.016503.0000000.0000000.0376470.0000000.0112211100
123253145.0000.0531783.7916670.0071430.02903112.2417170.0124611110
1232600.0000.05465.7500000.0000000.0213330.0000000.0113218110
1232700.0000.06184.2500000.0833330.0866670.0000000.01132113110
12328475.0000.015346.0000000.0000000.0210530.0000000.01122311100
1232900.0000.0321.2500000.0000000.0666670.0000000.0113212210

Duplicate rows

Most frequently occurring

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue# duplicates
800.000.010.00.20.20.00.03221110011
2500.000.010.00.20.20.00.0522131007
1700.000.010.00.20.20.00.0332311006
1900.000.010.00.20.20.00.0511131005
6400.000.010.00.20.20.00.0128139203005
1600.000.010.00.20.20.00.0332111004
4300.000.010.00.20.20.00.01122111004
100.000.010.00.20.20.00.0232331003
500.000.010.00.20.20.00.0311331003
700.000.010.00.20.20.00.0311811003